In modern gas turbines, detection of events, such as a shaft breakage event, can be performed by monitoring engine parameters using suitable sensing devices. The measurements are then sent to a control system, which applies detection logic to the data to determine if a predefined event signature is present. In particular, a derivative of the sensed signals is typically computed in order to determine the rate of change of the monitored engine parameters.
However, the sensed signals often contain noise components, such as steady state and transient noise components. When the derivative of a given sensed signal is taken, the resulting rate of change signal greatly amplifies any small noise component of the underlying sensed signal. The event signatures are in turn rendered undetectable within the noise floor. In order to remove the noise, traditional real-time filters may be applied to the sensed signals. Still, such filtering also induces significant signal delays, which prove unacceptable for high speed event detection, such as detection of shaft breakage events.
There is therefore a need for an improved system and method for conditioning noisy signals.